import pandas as pd
import seaborn as sns
import plotly.express as px
import numpy as np

import matplotlib.pyplot as plt

import plotly.io as pio
pio.renderers.default = "plotly_mimetype+notebook"

Matplotlib

For this excercise, we have written the following code to load the stock dataset built into plotly express.

stocks = px.data.stocks()
stocks.head()

	date	GOOG	AAPL	AMZN	FB	NFLX	MSFT
0	2018-01-01	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000
1	2018-01-08	1.018172	1.011943	1.061881	0.959968	1.053526	1.015988
2	2018-01-15	1.032008	1.019771	1.053240	0.970243	1.049860	1.020524
3	2018-01-22	1.066783	0.980057	1.140676	1.016858	1.307681	1.066561
4	2018-01-29	1.008773	0.917143	1.163374	1.018357	1.273537	1.040708

Question 1:

Select a stock and create a suitable plot for it. Make sure the plot is readable with relevant information, such as date, values.

# YOUR CODE HERE

fig, ax = plt.subplots()

ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r')
ax.set_xticks(stocks.loc[::10,'date'])

#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)
# set title
ax.set_title('Evolution of Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
plt.show()

Question 2:

You've already plot data from one stock. It is possible to plot multiples of them to support comparison.
To highlight different lines, customise line styles, markers, colors and include a legend to the plot.

# YOUR CODE HERE
fig, ax = plt.subplots()

ax.plot(stocks.loc[:,'date'],stocks.loc[:,'GOOG'], 'c', label='GOOG')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AAPL'], 'grey', label='AAPL')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'AMZN'], 'y', label='AMZN')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'FB'], 'b', label='FB')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'NFLX'], 'r', label='NFLX')
ax.plot(stocks.loc[:,'date'],stocks.loc[:,'MSFT'], 'g', label='MSFT')

ax.set_xticks(stocks.loc[::10,'date'])

#make the figure longer
fig.set_figwidth(10)
#display date vertically
plt.xticks(rotation=90)

# set title
ax.set_title('Evolution of GAFAM + Netflix stocks')
# horizontal axis
ax.set_xlabel('Date')
# vertical axis
ax.set_ylabel('Stock value (% of initial value)')
#legend
plt.legend()
plt.show()

Seaborn

First, load the tips dataset

tips = sns.load_dataset('tips')
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

Question 3:

Let's explore this dataset. Pose a question and create a plot that support drawing answers for your question.

Some possible questions:

• Are there differences between male and female when it comes to giving tips?
• What attribute correlate the most with tip?

# YOUR CODE HERE

# QUESTION : Which day of the week is the most profitable tip-wise ?
# ANSWER : Show the tip repartition by day 

fig, ax = plt.subplots()
sns.boxplot(x='day', y='tip', data=tips, showmeans=True)
ax.set_ylabel('Tip (in euro)')
plt.show()

Plotly Express

Question 4:

Redo the above exercises (challenges 2 & 3) with plotly express. Create diagrams which you can interact with.

The stocks dataset

Hints:

• Turn stocks dataframe into a structure that can be picked up easily with plotly express

# YOUR CODE HERE
fig = px.line(stocks, x="date", y=['GOOG', 'AAPL', 'AMZN', 'FB', 'MSFT', 'NFLX'], markers=True)
fig.show()

The tips dataset

# YOUR CODE HERE
fig = px.box(tips, x='day', y='tip')
fig.show()

Question 5:

Recreate the barplot below that shows the population of different continents for the year 2007.

Hints:

• Extract the 2007 year data from the dataframe. You have to process the data accordingly
• use plotly bar
• Add different colors for different continents
• Sort the order of the continent for the visualisation. Use axis layout setting
• Add text to each bar that represents the population

#load data
df = px.data.gapminder()
df.head()

	country	continent	year	lifeExp	pop	gdpPercap	iso_alpha	iso_num
0	Afghanistan	Asia	1952	28.801	8425333	779.445314	AFG	4
1	Afghanistan	Asia	1957	30.332	9240934	820.853030	AFG	4
2	Afghanistan	Asia	1962	31.997	10267083	853.100710	AFG	4
3	Afghanistan	Asia	1967	34.020	11537966	836.197138	AFG	4
4	Afghanistan	Asia	1972	36.088	13079460	739.981106	AFG	4

# YOUR CODE HERE
df_2007 = df.query('year==2007')
df_2007_new = df_2007.groupby('continent').sum()
df_2007_new = df_2007_new.reset_index()


fig = px.bar(df_2007_new, x="pop", y='continent', orientation='h', color = 'continent', text = 'pop')
fig.update_yaxes(categoryorder="min ascending")
fig.show()